Objective:　Clinically,　patients　in　a　coma　after　cardiac　arrest　are　given　the　prognosis　of　＂neurological　recovery＂　to　minimize　discrepancies　in　opinions　and　reduce　judgment　errors.　This　study　aimed　to　analyze　the　background　patterns　of　electroencephalogram　(EEG)　signals　from　such　patients　to　identify　the　key　indicators　for　assessing　the　prognosis　after　coma.　Approach:　Standard　machine　learning　models　were　applied　sequentially　as　feature　selectors　and　filters.　CatBoost　demonstrated　superior　performance　as　a　classification　method　compared　to　other　approaches.　In　addition,　Shapley　additive　explanation　(SHAP)　values　were　utilized　to　rank　and　analyze　the　importance　of　the　features.　Results:　Our　results　indicated　that　the　three　different　EEG　features　helped　achieve　a　fivefold　cross-validation　receiver-operating　characteristic　(ROC)　of　0.87.　Our　evaluation　revealed　that　functional　connectivity　features　contribute　the　most　to　classification　at　70%.　Among　these,　low-frequency　long-distance　functional　connectivity　(45%)　was　associated　with　a　poor　prognosis,　whereas　high-frequency　short-distance　functional　connectivity　(25%)　was　linked　with　a　good　prognosis.　Burst　suppression　ratio　is　20%,　concentrated　in　the　left　frontal-temporal　and　right　occipital-temporal　regions　at　high　thresholds　(10/15　mV),　demonstrating　its　strong　discriminative　power.　Significance:　Our　research　identifies　key　electroencephalographic　(EEG)　biomarkers,　including　low-frequency　connectivity　and　burst　suppression　thresholds,　to　improve　early　and　objective　prognosis　assessments.　By　integrating　machine　learning　(ML)　algorithms,　such　as　Gradient　Boosting　Models　and　Support　Vector　Machines,　with　SHAP-based　feature　visualization,　robust　screening　methods　were　applied　to　ensure　the　reliability　of　predictions.　These　findings　provide　a　clinically　actionable　framework　for　advancing　neurological　prognosis　and　optimizing　patient　care.